In [24]:
import pandas as pd 
import numpy as np 
import altair as alt
alt.data_transformers.enable("vegafusion")
Out[24]:
DataTransformerRegistry.enable('vegafusion')

Grid Savers: Potential Benifits to Utilize V2G¶

Using a Vehicle to Grid (V2G) system, utilities can benifit from lower residential demands during peak hours, which cuts the cost of purchasing electricity in the wholesale power market. In this report, we are interested in simulating the implementastion of such technology.

Assumptions¶

We are assuming:

  • The capacity of hardware, software, and regulation is supportive for this technology to implement
  • The reduce in demand will not effect wholesale electricity pricing

Data¶

Local Marginal Price (LMP) of the Day Ahead Market (DAM) in CAISO on May 12nd.

In [25]:
df = pd.read_csv('data/512pre.csv')
df['INTERVALSTARTTIME'] = pd.to_datetime(df['INTERVALSTARTTIME'])
df['Hour_Label'] = df['INTERVALSTARTTIME'].dt.strftime('%H:%M') + '-' + (df['INTERVALSTARTTIME'] + pd.Timedelta(hours=1)).dt.strftime('%H:%M')
df.head()
Out[25]:
OPR_DT NODE MW INTERVALSTARTTIME INTERVALENDTIME Hour_Label
0 2025-05-12 AFPR_1_TOT_GEN-APND 42.62938 2025-05-12 00:00:00 2025-05-12 01:00:00 00:00-01:00
1 2025-05-12 AFPR_1_TOT_GEN-APND 40.00993 2025-05-12 01:00:00 2025-05-12 02:00:00 01:00-02:00
2 2025-05-12 AFPR_1_TOT_GEN-APND 37.27168 2025-05-12 02:00:00 2025-05-12 03:00:00 02:00-03:00
3 2025-05-12 AFPR_1_TOT_GEN-APND 37.20192 2025-05-12 03:00:00 2025-05-12 04:00:00 03:00-04:00
4 2025-05-12 AFPR_1_TOT_GEN-APND 39.59306 2025-05-12 04:00:00 2025-05-12 05:00:00 04:00-05:00

Below is a comprehensive overview of all California LMPs.

In [26]:
chart = alt.Chart(df).mark_line(interpolate='step-after').encode(
    x=alt.X('INTERVALSTARTTIME:T', title='Time Interval'),
    y=alt.Y('MW:Q', title='Price ($/MWh)'),
    color='NODE:N',
    tooltip=['NODE', 'Hour_Label', 'MW']
).properties(
    title='Day-Ahead LMP ($/MWh) Over Time by Node',
    width=800,
    height=400
).interactive()

chart
Out[26]:

Below is the LMP of node- AFPR_1_TOT_GEN-APND.

In [ ]:
node_df = df[df['NODE'] == 'AFPR_1_TOT_GEN-APND'].copy()
node_df = node_df.sort_values('INTERVALSTARTTIME').reset_index(drop=True)

chart = alt.Chart(node_df).mark_line(interpolate='step-after').encode(
    x='INTERVALSTARTTIME:T',
    y='MW:Q',
).properties(
    title='Day-Ahead LMP ($/MWh) for Node- AFPR_1_TOT_GEN-APND',
    width=800,
    height=400
)

chart
Out[ ]:

Simulation¶

Let's simulate an V2G technology implementation.

Below is Useable battery capacity of full electric vehicles

Model Useable Battery Capacity (kWh)
S Dual Motor 95
S Plaid 95
X Dual Motor 95
X Plaid 95
Y Long Range AWD 75
Y Long Range AWD Launch Series 75
Y Long Range RWD 75
3 Performance 75
3 Long Range Dual Motor 75
3 Long Range RWD 75
Y RWD 60
3 57.5

Conclusion:¶

  • This simulation calculated how much utilities can benifit from arbitrage
  • This simulation also provide a reference point

Limitations¶

  • This simulation did not consider potential costs to implement such technology
  • This simulation also did not consider the Real-Time Market
  • There is a lack of information on utilities' purchase at each node, therefore